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Comment on Running AI is so expensive that Amazon will probably charge you to use Alexa in future, says outgoing exec

stevedidwhat_infosec@infosec.pub ⁨1⁩ ⁨year⁩ ago

Same. I’ve already got an entire setup between gpt with customizable system level prompting capabilities and it uses custom voice models I’ve trained over at eleven labs

Now I just gotta slap my lil monsters phat ass into a raspberry pi and then destroy the fuck out of my Alexa devices and ship em to Jeff bozo

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StubbornCassette8@feddit.nl ⁨1⁩ ⁨year⁩ ago
Can you share details? Been thinking of doing this with a new PC build. Curious what your performance and specs are.

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- Silentrizz@lemmy.world ⁨1⁩ ⁨year⁩ ago
  +1 interest
  
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- stevedidwhat_infosec@infosec.pub ⁨1⁩ ⁨year⁩ ago
  You shouldn’t need anything really, all the components run via cloud services so you just need a network connection.
  
  That’s why it’ll run just fine on a cheap pi model
  
  Essentially the script in Python just sends api requests directly to OpenAI and returns the AI response. Next I just pass that response to the elevenlabs api and play that audio binary stream via any library that supports audio playback.
  
  (That last bit is what I’ll have to toy around with on a pi but, I’m not worried about finding a suitable option, there’s lots of libraries out there)
  
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  - StubbornCassette8@feddit.nl ⁨1⁩ ⁨year⁩ ago
    Oh wait, I think I misunderstood. I thought you had local language models running on your computer. I have seen that be discusses before with varying results.
    
    Last time I tried running my own model was in the early days of the Llama release and randomness it on a RTX 3060. The Spend of delivery was much shower that OpenAI’s API and the material was way off.
    
    It doesn’t have to be perfect, but I’d like to do my own API calls from a remote device phoning home instead of OpenAI’s servers. Using my own documents as a reference would be a plus to, just to keep my info private and still accessible by the LLM.
    
    Didn’t know about Elevenlabs. Checking them out soon.
    
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    stevedidwhat_infosec@infosec.pub ⁨1⁩ ⁨year⁩ ago
    That could be fun! I’ve made and trained my own models prior but I find that getting the right amount of data (in terms of both size and diversity to ensure features are orthogonal out of the gate) can be pretty tough.
    
    If you don’t get that right balance of size and diversity in your data, that efficacy upper limit is gonna be way lower than you’d like, but you might have some good data sets laying around I got no clue ^_^
    
    Lemmy know how it goes!
    
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